from datetime import datetime
from IPython.display import display
from IPython.display import Markdown as MD
current_date = datetime.now().strftime('%Y-%m-%d')
version = datetime.now().strftime('%Y%m%d.%H%M')
display(MD(f"**Date:** {current_date}"))
display(MD(f"**version:** {version}"))

# this code to will import all the things i need for this notebook

import os
import re
import math

import numpy as np
import pandas as pd

# for the notebook rendering 
from IPython.display import display, HTML
from IPython.display import Markdown as MD

# Graphs and Charts
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go

# pandas Settings/Options
pd.set_option("display.max_rows", None) 
pd.set_option("display.max_columns", None)
pd.set_option('display.width', 9000)
pd.set_option('max_colwidth', 400)
pd.set_option('display.float_format', '{:.3f}'.format)

# colormap 
heatmapCM = sns.color_palette('Spectral_r', as_cmap=True)


## directories 
DIR = os.getcwd()
print(f'{DIR=}')

DataDIR = os.path.join(DIR,'data')
OutDIR = os.path.join(DIR,'docs')

if not os.path.exists(DataDIR):
    print('***DATA FOLDER IS MISSING***')

if not os.path.exists(OutDIR):
    os.makedirs(OutDIR)

DIR='C:\\Users\\JGarza\\GitHub\\eggcellent_data_analysis'

# Define the nodes (labels)
node_labels = [
    "🐔 Chicken", "🥚 Eggs", "🍳🍗 Market/Food", "🐣 Hatch", "🐔 Chickens"
]

# Define the links (source, target, and values)
source = [  0,  1,  1,  3,  4,  4 ]  # Chicken -> Eggs, Eggs -> Market/Food, etc.
target = [  1,  2,  3,  4,  2,  0 ]  # Eggs -> Market/Food, Eggs -> Hatch, etc.
values = [100, 50, 50, 50, 50, 50 ]  # Flow values

# Create the Sankey diagram
fig = go.Figure(go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.5),
        label=node_labels
    ),
    link=dict(
        source=source,
        target=target,
        value=values
    )
))

fig.update_layout(title_text="Chicken and Egg Sankey Diagram", font_size=12,template="plotly_dark")
# Set title and show figure
fig.show()

months = {
    'JAN': 1,
    'FEB': 2,
    'MAR': 3,
    'APR': 4,
    'MAY': 5,
    'JUN': 6,
    'JUL': 7,
    'AUG': 8,
    'SEP': 9,
    'OCT': 10,
    'NOV': 11,
    'DEC': 12
}

def getMonthNum(mon:str):
    for m in months.keys():
        if m.upper() in mon:
            return months[m]

print(getMonthNum('JAN'))
print(getMonthNum('JUN'))
print(getMonthNum('FIRST OF DEC'))

1
6
12

# EggLayers
el = pd.read_csv(os.path.join(DataDIR,'EggLayers.csv'))

el = el[['Year','Period','Value']]
el['Month'] =  el.Period.apply(getMonthNum)
el['Day'] = 1

el['date'] = pd.to_datetime(el[['Year','Month','Day']])
el = el[['date','Value']]

el['Value'] = el['Value'].str.replace(',', '', regex=True)
el['Value'] = pd.to_numeric(el['Value'])
el['Value'] /=1_000_000_000

el = el.sort_values(by='date',ascending=True)

display(el.head(5))
display(el.tail(5))

# Dollars per Dozen
dd = pd.read_csv(os.path.join(DataDIR,'DollarsPerDozen.csv'))

dd['Month'] = dd.Period.apply(getMonthNum)
dd['Day'] = 1

dd['date'] = pd.to_datetime(dd[['Year','Month','Day']])

dd = dd[['date','Value']]

dd = dd.sort_values(by='date',ascending=True)

display(dd.head(5))
display(dd.tail(5))

# US Population
up = pd.read_csv(os.path.join(DataDIR,'USPop.csv'))

up['Month'] = 1
up['Day'] = 1
up['date'] = pd.to_datetime(up[['Year','Month','Day']])

up = up[['date','Population','Growth Rate']]

up['Population'] = up['Population'].str.replace(',', '', regex=True)
up['Population'] = pd.to_numeric(up['Population'])
up['Population'] /=1_000_000_000

up = up.sort_values(by='date',ascending=True)

display(up.head(5))
display(up.tail(5))

# Create Plotly figure
fig = go.Figure()

# First trace (Primary Y-axis)
fig.add_trace(go.Scatter(
    x=el['date'], 
    y=el['Value'], 
    mode='lines', 
    name='EggLayers', 
    yaxis="y1"
    ))

# Second trace (Second Y-axis)
fig.add_trace(go.Scatter(
    x=dd['date'], 
    y=dd['Value'], 
    mode='lines', 
    name='Dollars Per Dozen', 
    yaxis="y2"
    ))

# Third trace (Third Y-axis)
fig.add_trace(go.Scatter(
    x=up['date'], 
    y=up['Population'], 
    mode='lines', 
    name='US Pop.', 
    yaxis="y3"
    ))

# Layout configuration
fig.update_layout(
    title="EggLayers x Dollars Per Dozen x US Population",
    xaxis=dict(title="X Axis"),
    
    # First Y-axis (left side)
    yaxis=dict(title="EggLayers"),
    
    # Second Y-axis (right side)
    yaxis2=dict(title="Dollars Per Dozen",
                overlaying="y", side="right"),
    
    # Third Y-axis (right side but slightly shifted)
    yaxis3=dict(title="US Pop.",
                overlaying="y", side="right", anchor="x", position=1),
    
    template="plotly_dark",
    height=750 ,
)

# Show the plot
fig.show()

	date	Value
197	2008-01-01	0.346
196	2008-02-01	0.344
200	2008-03-01	0.343
193	2008-04-01	0.342
201	2008-05-01	0.341

	date	Value
12	2024-09-01	0.372
11	2024-10-01	0.377
10	2024-11-01	0.376
3	2024-12-01	0.376
0	2025-01-01	0.369

	date	Value
293	2000-12-01	0.674
285	2001-01-01	0.507
284	2001-02-01	0.501
288	2001-03-01	0.517
281	2001-04-01	0.484

	date	Value
1	2024-08-01	3.300
11	2024-09-01	1.960
10	2024-10-01	2.720
9	2024-11-01	3.220
2	2024-12-01	4.610

	date	Population	Growth Rate
17	2008-01-01	0.306	0.97%
16	2009-01-01	0.309	0.92%
15	2010-01-01	0.311	0.87%
14	2011-01-01	0.314	0.87%
13	2012-01-01	0.317	0.88%

Eggcellent_Data_Analysis¶

Set Up¶

Sources¶

Major Bird Flu Culling Events in the U.S.¶

	date	Population	Growth Rate
4	2021-01-01	0.337	0.31%
3	2022-01-01	0.338	0.38%
2	2023-01-01	0.340	0.50%
1	2024-01-01	0.342	0.53%
0	2025-01-01	0.344	0.52%